Lipid droplet detection in histopathological images using reinforcement learning
The identification and analysis of lipid droplets in pathological images are pivotal owing to the variations in their size, shading and shape. The development of an artificial intelligence-based automatic detection method can facilitate the analysis of complex pathological images and provide essenti...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Biotechnology & Biotechnological Equipment |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/13102818.2024.2338433 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The identification and analysis of lipid droplets in pathological images are pivotal owing to the variations in their size, shading and shape. The development of an artificial intelligence-based automatic detection method can facilitate the analysis of complex pathological images and provide essential support to pathologists. In this paper, we introduce a novel approach for the automated detection of lipid droplets, employing a limited set of images and a modest application of reinforcement learning (RL). Several filters tailored to lipid droplet size and contrast are used in combination. Through the combination of multiple filters using RL, potential lipid droplet regions are identified within pathological images. Subsequently, a random forest classifier is employed to distinguish between normal and lipid droplet images. Evaluation guided by the expertise of two pathologists with over 10 years of clinical experience indicated the hierarchical extraction of lipid droplets with consistent size and shading in pathological tissue images utilizing RL. The proposed method successfully detected lipid droplets in pathological images and facilitated the determination of both the quantity and distribution of lipid droplets within cells. The results highlight the efficacy of the approach in lipid droplet detection. This method is also useful for small to medium-sized fat droplets, which are relatively difficult for humans to detect from their morphology. |
|---|---|
| ISSN: | 1310-2818 1314-3530 |